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A multi-commodity network flow model for railway capacity optimization in case of line blockage

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  • Abbas Azadi Moghaddam Arani
  • Fariborz Jolai
  • Mohammad Mahdi Nasiri

Abstract

In this study, a multi-commodity network flow model is proposed to optimize the railroad capacity under temporary line blockage. The proposed model enables the assessment of residual railroad capacity under heterogeneous traffic condition. The model searches for an optimized train timetable to maximize the number of possible train paths by maintaining acceptable percentages of delayed trains. Computational experiments are conducted on instances of Iran railway to evaluate the performance of the model regarding computational efficiency and solution quality. The outcomes demonstrate an average optimality gap of about 3.7% which quantifies the effectiveness of the optimization model within a reasonable computational time. The output of the optimization model has been compared with the UIC406 (International Union of Railway) standard. The proposed optimization model could generate a more realistic solution in comparison with UIC406 method. According to the obtained result, the maximum capacity of the rail line increases by approximately 26.7% compared with UIC 406 code.

Suggested Citation

  • Abbas Azadi Moghaddam Arani & Fariborz Jolai & Mohammad Mahdi Nasiri, 2019. "A multi-commodity network flow model for railway capacity optimization in case of line blockage," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 7(4), pages 297-320, October.
    • Handle: RePEc:taf:tjrtxx:v:7:y:2019:i:4:p:297-320
    DOI: 10.1080/23248378.2019.1571450
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    Citations

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    Cited by:

    1. Wenliang Zhou & Sha Li & Jing Kang & Yu Huang, 2022. "Capacity-Oriented Train Scheduling of High-Speed Railway Considering the Operation and Maintenance of Rolling Stock," Mathematics, MDPI, vol. 10(10), pages 1-30, May.
    2. Anna Dolinayova & Vladislav Zitricky & Lenka Cerna, 2020. "Decision-Making Process in the Case of Insufficient Rail Capacity," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    3. Miguel Campaña & Esteban Inga & Jorge Cárdenas, 2021. "Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities," Energies, MDPI, vol. 14(16), pages 1-16, August.
    4. Trivella, Alessio & Corman, Francesco & Koza, David F. & Pisinger, David, 2021. "The multi-commodity network flow problem with soft transit time constraints: Application to liner shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    5. Khodakaram Salimifard & Sara Bigharaz, 2022. "The multicommodity network flow problem: state of the art classification, applications, and solution methods," Operational Research, Springer, vol. 22(1), pages 1-47, March.

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